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Scholarly Works (7 results)

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UC Irvine Electronic Theses and Dissertations (2016)

We investigated a new computational model of speech production, the Semantic-Lexical-Auditory-Motor Model (SLAM), which was designed to test a critical assumption of the Hierarchical State Feedback Control theory (Hickok, 2012): specifically, that speech production relies on the coordination of dual representations in auditory and motor cortices, with auditory representations serving as targets during speech planning. The computational details are based on the interactive, two-step lexical retrieval model of Foygel and Dell (2000); our novel architecture allows us to predict the consequences of different patterns of damage among the proposed speech representations. The additional model structure is expected to better explain conduction aphasia in particular. We analyzed archived picture naming data from 255 people with aphasia in Philadelphia, PA, in addition to new data from another 95 people with aphasia in Columbia, SC. We found that the SLAM model made adequate predictions generally, and it did improve the fit to data from conduction patients specifically and in the expected manner. We also analyzed neuroanatomical data in the form of lesion masks standardized to a template for 83 of the participants from the SC cohort. Although we were unable to replicate a study to localize brain regions where damage leads to a significant increase in particular error types, a behavioral comparison of the different cohorts revealed the potential sampling variability that exists in the aphasia population. Next, we developed a Bayesian approach to estimating the parameters of the lexical network, providing a more comprehensive assessment of the model’s quality. Additionally, we simulated word and nonword repetition tasks with our network, generating new predictions for a subset of 28 people with aphasia and unimpaired hearing. We found that the 4-parameter SLAM model could simultaneously find good fits for the frequencies of 6 naming and 3 nonword repetition response types while also correctly predicting a novel set of 6 word repetition response types. The conduction patients' data was best explained by strong lexical-auditory and weak auditory-motor connections. Our results demonstrate that the assumption of coordinated speech representations in auditory and motor cortices can lead to viable predictions of speech production behavior in aphasia.

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Cover page: Computational Modeling of Speech Production and AphasiaCreative Commons 'BY' version 4.0 license
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UC Irvine Previously Published Works (2016)
Speech production is studied from both psycholinguistic and motor-control perspectives, with little interaction between the approaches. We assessed the explanatory value of integrating psycholinguistic and motor-control concepts for theories of speech production. By augmenting a popular psycholinguistic model of lexical retrieval with a motor-control-inspired architecture, we created a new computational model to explain speech errors in the context of aphasia. Comparing the model fits to picture-naming data from 255 aphasic patients, we found that our new model improves fits for a theoretically predictable subtype of aphasia: conduction. We discovered that the improved fits for this group were a result of strong auditory-lexical feedback activation, combined with weaker auditory-motor feedforward activation, leading to increased competition from phonologically related neighbors during lexical selection. We discuss the implications of our findings with respect to other extant models of lexical retrieval.
    Cover page: Bridging computational approaches to speech production: The semantic–lexical–auditory–motor model (SLAM)
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    UC Irvine Previously Published Works (2016)
    In a previous publication, we presented a new computational model called SLAM (Walker & Hickok, Psychonomic Bulletin & Review doi: 10.3758/s13423-015-0903 ), based on the hierarchical state feedback control (HSFC) theory (Hickok Nature Reviews Neuroscience, 13(2), 135-145, 2012). In his commentary, Goldrick (Psychonomic Bulletin & Review doi: 10.3758/s13423-015-0946-9 ) claims that SLAM does not represent a theoretical advancement, because it cannot be distinguished from an alternative lexical + postlexical (LPL) theory proposed by Goldrick and Rapp (Cognition, 102(2), 219-260, 2007). First, we point out that SLAM implements a portion of a conceptual model (HSFC) that encompasses LPL. Second, we show that SLAM accounts for a lexical bias present in sound-related errors that LPL does not explain. Third, we show that SLAM's explanatory advantage is not a result of approximating the architectural or computational assumptions of LPL, since an implemented version of LPL fails to provide the same fit improvements as SLAM. Finally, we show that incorporating a mechanism that violates some core theoretical assumptions of LPL-making it more like SLAM in terms of interactivity-allows the model to capture some of the same effects as SLAM. SLAM therefore provides new modeling constraints regarding interactions among processing levels, while also elaborating on the structure of the phonological level. We view this as evidence that an integration of psycholinguistic, neuroscience, and motor control approaches to speech production is feasible and may lead to substantial new insights.
      Cover page: Evaluating quantitative and conceptual models of speech production: how does SLAM fare?
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      Proceedings of the Annual Meeting of the Cognitive Science Society, Volume 33 (2011)
        Cover page: A Tale of Two Semantic Systems: Taxonomic and Thematic Knowledge
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        UC Irvine Previously Published Works (2021)
        Purpose This study investigated the relationship between word production rates (WPRs) and phonological error rates (PERs) in generative and responsive tasks in logopenic progressive aphasia (lvPPA). We examined whether a portion of the reduced WPR during generative tasks related directly to phonological impairments affecting PER on all tasks, irrespective of other task differences that contributed to WPR. Method Two cognitive psychometric models were hypothesized and fit to the total number of words produced and the number of phonological errors produced by 22 participants on 10 tasks. Bayesian inference was used to construct posterior distributions of participant ability and task difficulty parameters. Model fit statistics were compared. Association strengths for average generative WPR and average responsive PER were also evaluated with linear least-squares regression. Results Average generative WPR and average responsive PER were significantly associated (r = -.77, p = .00002). A cognitive psychometric model that assumed reduced WPR on generative tasks reflects a portion of general phonological impairment yielded better fit than a model that ignored performance differences between generative and responsive tasks. Generative fluency tasks that elicited few phonological errors still reflected phonological impairment, via suppression. Individual participants were estimated to suppress between 62% and 93% of phonological errors on generative tasks that would have emerged on responsive tasks. Conclusions Suppression of phonological errors may present as decreased WPR on generative tasks in lvPPA. Failure to account for this suppression tendency may lead to overestimation of phonological ability. The findings indicate the need to account for task demands in assessing lvPPA.
          Cover page: A Cognitive Psychometric Investigation of Word Production and Phonological Error Rates in Logopenic Progressive Aphasia.
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          • Peer Reviewed
          UC Irvine Previously Published Works (2022)
          PURPOSE: We present a 20-item naming test, the Severity-Calibrated Aphasia Naming Test (SCANT), that can serve as a proxy measure for an aphasia severity scale that is derived from a thorough test battery of connected speech production, single-word production, speech repetition, and auditory verbal comprehension. METHOD: We use lasso regression and cross-validation to identify an optimal subset from a set of 174 pictures to be named for prediction of aphasia severity, based on data from 200 participants with left-hemisphere stroke who were quasirandomly selected to represent the full impairment scale. Data from 20 healthy controls (i.e., participant caretakers/spouses) were also analyzed. We examine interrater reliability, test-retest reliability, sensitivity and specificity to the presence of aphasia, sensitivity to therapy gains, and external validity (i.e., correlation with aphasia severity measures) for the SCANT. RESULTS: The SCANT has extremely high interrater reliability, and it is sensitive and specific to the presence of aphasia. We demonstrate the superiority of predictions based on the SCANT over those based on the full set of naming items. We estimate a 15% reduction in power when using the SCANT score versus the full test batterys aphasia severity score as an outcome measure; for example, to maintain the same power to detect a significant group average change in aphasia severity, a study with 25 participants using the full test battery to measure treatment effectiveness would require 30 participants if the SCANT were to be used as the testing instrument instead. CONCLUSION: We provide a linear model to convert SCANT scores to aphasia severity scores, and we identify a change score cutoff of four SCANT items to obtain a high degree of confidence based on test-retest SCANT data and the modeled relation between SCANT and aphasia severity scores. SUPPLEMENTAL MATERIAL: https://doi.org/10.23641/asha.21476871.
            Cover page: The Severity-Calibrated Aphasia Naming Test.
            • Article
            • Peer Reviewed
            UC Irvine Previously Published Works (2023)
            BACKGROUND: Aphasia therapy is an effective approach to improve language function in chronic aphasia. However, it remains unclear what prognostic factors facilitate therapy response at the individual level. Here, we utilized data from the POLAR (Predicting Outcomes of Language Rehabilitation in Aphasia) trial to (a) determine therapy-induced change in confrontation naming and long-term maintenance of naming gains and (b) examine the extent to which aphasia severity, age, education, time postonset, and cognitive reserve predict naming gains at 1 week, 1 month, and 6 months posttherapy. METHOD: A total of 107 participants with chronic (≥ 12 months poststroke) aphasia underwent extensive case history, cognitive-linguistic testing, and a neuroimaging workup prior to receiving 6 weeks of impairment-based language therapy. Therapy-induced change in naming performance (measured as raw change on the 175-item Philadelphia Naming Test [PNT]) was assessed 1 week after therapy and at follow-up time points 1 month and 6 months after therapy completion. Change in naming performance over time was evaluated using paired t tests, and linear mixed-effects models were constructed to examine the association between prognostic factors and therapy outcomes. RESULTS: Naming performance was improved by 5.9 PNT items (Cohens d = 0.56, p < .001) 1 week after therapy and by 6.4 (d = 0.66, p < .001) and 7.5 (d = 0.65, p < .001) PNT items at 1 month and 6 months after therapy completion, respectively. Aphasia severity emerged as the strongest predictor of naming improvement recovery across time points; mild (ß = 5.85-9.02) and moderate (ß = 9.65-11.54) impairment predicted better recovery than severe (ß = 1.31-3.37) and very severe (ß = 0.20-0.32) aphasia. Age was an emergent prognostic factor for recovery 1 month (ß = -0.14) and 6 months (ß = -0.20) after therapy, and time postonset (ß = -0.05) was associated with retention of naming gains at 6 months posttherapy. CONCLUSIONS: These results suggest that therapy-induced naming improvement is predictable based on several easily measurable prognostic factors. Broadly speaking, these results suggest that prognostication procedures in aphasia therapy can be improved and indicate that personalization of therapy is a realistic goal in the near future. SUPPLEMENTAL MATERIAL: https://doi.org/10.23641/asha.22141829.
              Cover page: Predicting Outcomes of Language Rehabilitation: Prognostic Factors for Immediate and Long-Term Outcomes After Aphasia Therapy.
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